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Topics of Google Professional Cloud DevOps Engineer Exam

Candidates must know the exam topics before they start preparation because it will help them in hitting the core. Our Professional Cloud DevOps Engineer Dumps will include the following topics:

Applying site reliability engineering principles to a service

1.2 Manage service life cycle:
Agree to consequences of not meeting the error budget
Balance change, velocity, and reliability of the service:
Discover SLIs (availability, latency, etc.)
Manage a service (e.g., introduce a new service, deploy it, maintain and retire it)
Define SLOs and understand SLAs
Toil automation

Building and implementing CI/CD pipelines for a service

Immutable artifacts with Container Registry
Testing a new version with Spinnaker
CI/CD pipeline triggers with Cloud Source Repositories, Cloud Build GitHub App, Cloud Pub/Sub
Design CI/CD pipelines:
Configure deployment processes (e.g., approval flows)
Artifact repositories with Container Registry

Implementing service monitoring strategies

Understanding the logging exclusion vs. logging export
Viewing logs in the GCP Console
Viewing export logs in Cloud Storage and BigQuery
Manage application logs:
Collecting third-party and structured logs with Stackdriver Logging, Fluentd
Sending logs to an external logging platform
Use metric explorer for ad hoc metric analysis
Enabling data access logs (e.g., Cloud Audit Logs)
Set ACL to restrict export configuration with IAM, Stackdriver Logging
Sending application logs directly to Stackdriver API with Stackdriver Logging
Collecting logs from Compute Engine, GKE with Stackdriver Logging, Fluentd
Enabling VPC flow logs
Set ACL to allow metric writing for custom metrics with IAM, Stackdriver Monitoring
Collecting GKE/Kubernetes metrics

Optimizing service performance

Evaluate and understand user impact (Stackdriver Service Monitoring for App Engine, Istio)
Utilize Stackdriver Trace/Profiler to profile performance characteristics
Manage preemptible VMs
Utilize Stackdriver to identify cloud resource utilization
Troubleshoot issues with the image/OS
Identify service performance issues:
TCO considerations
identify resource costs
Consider network pricing

Managing service incidents

Scaling response team and delegation
Coordinate roles and implement communication channels during a service incident:
Handle requests for impact assessment
Establish communications channels (email, IRC, Hangouts, Slack, phone, etc.)
Evaluate symptoms against probable causes; the rank probability of cause based on observed behavior
Identify alternatives to mitigate the issue
Perform an investigation to isolate the most likely actual cause
Record major changes in incident state (When mitigated? When all clear? etc.)

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Google Professional-Cloud-DevOps-Engineer Certification Exam is an excellent opportunity for IT professionals who want to validate their skills and expertise in cloud-based DevOps engineering. Google Cloud Certified - Professional Cloud DevOps Engineer Exam certification is designed to evaluate the candidate's comprehensive understanding of DevOps practices, Google Cloud technologies, and their ability to design, develop, and manage DevOps pipelines using Google Cloud technologies. By earning this certification, IT professionals can demonstrate their skills and expertise in the field of cloud-based DevOps engineering and advance their careers in this dynamic and rapidly evolving field.

Introduction to Google Professional Cloud DevOps Engineer Exam

Google Professional Cloud DevOps Engineer Exam is a certification exam that is conducted by Google to validates candidate knowledge and skills of working as a Professional Cloud DevOps engineer in the IT industry.

After passing this Professional Cloud DevOps Engineer Exam test, candidates get a certificate from Google that helps them to demonstrate their proficiency in Google Professional Cloud DevOps Engineer to their clients and employers.

Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q193-Q198):

NEW QUESTION # 193
You are investigating issues in your production application that runs on Google Kubernetes Engine (GKE).
You determined that the source Of the issue is a recently updated container image, although the exact change in code was not identified. The deployment is currently pointing to the latest tag. You need to update your cluster to run a version of the container that functions as intended. What should you do?

A. Create a new tag called stable that points to the previously working container, and change the deployment to point to the new tag.
B. Build a new container from a previous Git tag, and do a rolling update on the deployment to the new container.
C. Apply the latest tag to the previous container image, and do a rolling update on the deployment.
D. Alter the deployment to point to the sha2 56 digest of the previously working container.

Answer: D

NEW QUESTION # 194
As a Site Reliability Engineer, you support an application written in GO that runs on Google Kubernetes Engine (GKE) in production. After releasing a new version Of the application, you notice the application runs for about 15 minutes and then restarts. You decide to add Cloud Profiler to your application and now notice that the heap usage grows constantly until the application restarts. What should you do?

A. Increase the CPU limit in the application deployment.
B. Add Cloud Trace to the application, and redeploy.
C. Increase the memory limit in the application deployment.
D. Add high memory compute nodes to the cluster.

Answer: C

Explanation:
Explanation
The correct answer is B. Increase the memory limit in the application deployment.
The application is experiencing a memory leak, which means that it is allocating memory that is not freed or reused. This causes the heap usage to grow constantly until it reaches the memory limit of the pod, which triggers a restart by Kubernetes. Increasing the memory limit in the application deployment can help mitigate the problem by allowing the application to run longer before reaching the limit. However, this is not a permanent solution, as the memory leak will still occur and eventually exhaust the available memory. The best solution is to identify and fix the source of the memory leak in the application code, using tools like Cloud Profiler and pprof12.

NEW QUESTION # 195
Your company operates in a highly regulated domain that requires you to store all organization logs for seven years You want to minimize logging infrastructure complexity by using managed services You need to avoid any future loss of log capture or stored logs due to misconfiguration or human error What should you do?

A. Use Cloud Logging to configure an export sink at each project level to export all logs into Cloud Storage with a seven-year retention policy and Bucket Lock
B. Use Cloud Logging to configure an aggregated sink at the organization level to export all logs into Cloud Storage with a seven-year retention policy and Bucket Lock
C. Use Cloud Logging to configure an aggregated sink at the organization level to export all logs into a BigQuery dataset
D. Use Cloud Logging to configure an export sink at each project level to export all logs into a BigQuery dataset

Answer: B

NEW QUESTION # 196
You have a set of applications running on a Google Kubernetes Engine (GKE) cluster, and you are using Stackdriver Kubernetes Engine Monitoring. You are bringing a new containerized application required by your company into production. This application is written by a third party and cannot be modified or reconfigured. The application writes its log information to /var/log/app_messages.log, and you want to send these log entries to Stackdriver Logging. What should you do?

A. Write a script to tail the log file within the pod and write entries to standard output. Run the script as a sidecar container with the application's pod. Configure a shared volume between the containers to allow the script to have read access to /var/log in the application container.
B. Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.
C. Deploy a Fluentd daemonset to GKE. Then create a customized input and output configuration to tail the log file in the application's pods and write to Slackdriver Logging.
D. Install Kubernetes on Google Compute Engine (GCE> and redeploy your applications. Then customize the built-in Stackdriver Logging configuration to tail the log file in the application's pods and write to Stackdriver Logging.

Answer: C

Explanation:
https://cloud.google.com/architecture/customizing-stackdriver-logs-fluentd Besides the list of default logs that the Logging agent streams by default, you can customize the Logging agent to send additional logs to Logging or to adjust agent settings by adding input configurations. The configuration definitions in these sections apply to the fluent-plugin-google-cloud output plugin only and specify how logs are transformed and ingested into Cloud Logging. https://cloud.google.com/logging/docs/agent/logging/configuration#configure

NEW QUESTION # 197
Your Cloud Run application writes unstructured logs as text strings to Cloud Logging. You want to convert the unstructured logs to JSON-based structured logs. What should you do?

A. Configure the log agent to convert log text payload to JSON payload.
B. A Install a Fluent Bit sidecar container, and use a JSON parser.
C. Install the log agent in the Cloud Run container image, and use the log agent to forward logs to Cloud Logging.
D. Modify the application to use Cloud Logging software development kit (SDK), and send log entries with a jsonPay10ad field.

Answer: D

NEW QUESTION # 198
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